Flyway Analysis
Cecilia Nilsson
2017-12-01
Bird profiles from 70 weather radar stations in Europe. Weather radar data processed by vol2bird in bioRad, then vp_processing.
Data Summary
| Country | Source | Processed by | Version | Sites included | Time res | Range | Dealiased |
|---|---|---|---|---|---|---|---|
| Sweden | BALTRAD | Liesbeth | 0.3.13/15 | 10 | 15 min | 25km/40km* | no |
| Finland | BALTRAD | Liesbeth | 0.3.15 | 10 | 15 min | 25km | yes |
| The Netherlands | BALTRAD | Liesbeth | 0.3.15 | 2 | 15 min | 25km | no |
| Czech Republic | BALTRAD | Liesbeth | 0.3.15 | 2 | 15 min | 25km | yes |
| France | BALTRAD | Liesbeth | 0.3.13 | 15 | 15 min | 25km/40km* | no |
| Germany | DWD | Baptiste | 0.3.15 | 15 | 15 min | 25km | no |
| Poland | IMGW | Anna | 0.3.6 | 7 | 10 min | 25km | Some** |
| Belgium | KNMI? | Liesbeth | 0.3.16 | 3 | 5 min | 25km | no |
| Portugal | IPMA? | Pablo | 0.3.15 | 3 | 10 min | 25km | no |
| Bulgaria | ? | Hidde | 0.3.16 | 1 | 5 min | 25km | no |
| Catalonia | ? | Nadja | 0.3.16 | 2 | 2 min | 25km | no |
*40: sevar, seang, frbou, frmom
**plleg and plrze not
MTR
Filtering and rain
The data is filtered to only include nighttime (defined as between sunset and sunrise, calculated individually for at each site). Sep 18 and Oct 09 are excluded because they are not full nights (start/end at midnight). For each time stamp, we also indicate if it is raining or not. Rain is defined as more then 5 altitude bins with a DBZH of above 7.
flyway_all %>%
mutate(Time = substring(datetime, 12,16)) %>% #Add "time" column (from "datetime")
mutate(Country = substring(radar_id,1,2)) %>% #Add "country" column (from "radar_id")
mutate(date_of_sunset =
as.Date(as.character(date_of_sunset),'%Y%m%d')) %>% #Set "date_of_sunset" to date format
filter(day_night == "night") %>% #Filter out daytime timestamps
filter(date_of_sunset != "2016-09-18" &
date_of_sunset != "2016-10-09") %>% #Filter out start and end dates
group_by(radar_id, datetime)%>%
mutate(rain = ifelse(length(which(DBZH>7))>5, "yes", "no") #with in timestamp, set rain to "yes"/"no"
)->flyway_night MTR is then aggregated by summing over altitudes and averaging over night, to get the mean MTR per night. NA’s are ignored. Important to note here is that some radars have missing data within nights, and some are missing entire nights. Since we are mostly working with averages over the large data set, I don’t think this is necessarily a problem, but it’s up for discussion. 8 radars have one or two entire nights missing, while 4 have parts of a night missing. See a summary of the data coverage in the issue summary, or here for all the details of the data coverage.
So, sum MTR within each time stamp (over all heights) per site and date_of_sunset, then average the over the night for each site, each night. Also add what percentage of altitude profiles that contained rain:
flyway_night %>%
group_by(radar_id, date_of_sunset, datetime) %>% #For each site, night and timestap:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE), #Sum over all heights
rain = rain[1]) %>% #Keep rain yes/no in dataset
group_by(radar_id, date_of_sunset) %>% #For each site and night:
summarize(
mean_MTR = mean(sum_MTR, na.rm = TRUE), #Mean over night
percent_rain = (length(which(rain=="yes"))
/length(rain))*100 #Percentage of altitudeprofiles with rain
)->flyway_agg_mtr
Quick look at flyway_agg_mtr, 1 to 100:
| radar_id | date_of_sunset | mean_MTR | percent_rain |
|---|---|---|---|
| bejab | 2016-09-19 | 85.13902 | 55.384615 |
| bejab | 2016-09-20 | 69.47089 | 0.000000 |
| bejab | 2016-09-21 | 49.89622 | 0.000000 |
| bejab | 2016-09-22 | 46.88227 | 14.159292 |
| bejab | 2016-09-23 | 49.84186 | 0.000000 |
| bejab | 2016-09-24 | 26.14476 | 0.000000 |
| bejab | 2016-09-25 | 31.67191 | 0.000000 |
| bejab | 2016-09-26 | 92.81671 | 0.000000 |
| bejab | 2016-09-27 | 128.64264 | 0.000000 |
| bejab | 2016-09-28 | 75.17468 | 0.000000 |
| bejab | 2016-09-29 | 74.10825 | 8.029197 |
| bejab | 2016-09-30 | 41.30632 | 23.188406 |
| bejab | 2016-10-01 | 32.65174 | 75.000000 |
| bejab | 2016-10-02 | 91.71400 | 11.510791 |
| bejab | 2016-10-03 | 1025.04929 | 0.000000 |
| bejab | 2016-10-04 | 1140.40112 | 0.000000 |
| bejab | 2016-10-05 | 478.64720 | 0.000000 |
| bejab | 2016-10-06 | 265.00750 | 0.000000 |
| bejab | 2016-10-07 | 255.11656 | 0.000000 |
| bejab | 2016-10-08 | 326.43206 | 33.566434 |
| bewid | 2016-09-21 | 2371.76064 | 0.000000 |
| bewid | 2016-09-22 | 680.98369 | 0.000000 |
| bewid | 2016-09-23 | 275.52608 | 0.000000 |
| bewid | 2016-09-24 | 78.39796 | 0.000000 |
| bewid | 2016-09-25 | 295.97874 | 0.000000 |
| bewid | 2016-09-26 | 1331.49167 | 5.185185 |
| bewid | 2016-09-27 | 243.91098 | 0.000000 |
| bewid | 2016-09-28 | 39.12097 | 0.000000 |
| bewid | 2016-09-29 | 64.84973 | 2.919708 |
| bewid | 2016-09-30 | 11.09008 | 92.753623 |
| bewid | 2016-10-01 | 34.54364 | 44.927536 |
| bewid | 2016-10-02 | 266.53593 | 8.633093 |
| bewid | 2016-10-03 | 7254.63828 | 0.000000 |
| bewid | 2016-10-04 | 3657.00468 | 0.000000 |
| bewid | 2016-10-05 | 1364.67679 | 0.000000 |
| bewid | 2016-10-06 | 108.90162 | 0.000000 |
| bewid | 2016-10-07 | 619.64518 | 1.398601 |
| bewid | 2016-10-08 | 1329.47220 | 0.000000 |
| bezav | 2016-09-19 | 101.50319 | 0.000000 |
| bezav | 2016-09-20 | 157.68062 | 0.000000 |
| bezav | 2016-09-21 | 103.50118 | 0.000000 |
| bezav | 2016-09-22 | 62.61783 | 0.000000 |
| bezav | 2016-09-23 | 50.65719 | 0.000000 |
| bezav | 2016-09-24 | 37.29761 | 0.000000 |
| bezav | 2016-09-25 | 35.75472 | 0.000000 |
| bezav | 2016-09-26 | 224.63421 | 0.000000 |
| bezav | 2016-09-27 | 35.99292 | 0.000000 |
| bezav | 2016-09-28 | 32.47516 | 0.000000 |
| bezav | 2016-09-29 | 53.16129 | 1.550388 |
| bezav | 2016-09-30 | 29.78694 | 36.231884 |
| bezav | 2016-10-01 | 23.42420 | 16.071429 |
| bezav | 2016-10-02 | 171.66247 | 7.857143 |
| bezav | 2016-10-03 | 1908.87124 | 0.000000 |
| bezav | 2016-10-04 | 1210.42543 | 0.000000 |
| bezav | 2016-10-05 | 343.67656 | 0.000000 |
| bezav | 2016-10-06 | 70.06894 | 0.000000 |
| bezav | 2016-10-07 | 190.29835 | 0.000000 |
| bezav | 2016-10-08 | 283.07568 | 0.000000 |
| bgvar | 2016-09-19 | 290.30139 | 16.197183 |
| bgvar | 2016-09-20 | 347.65164 | 26.056338 |
| bgvar | 2016-09-21 | 317.36329 | 9.090909 |
| bgvar | 2016-09-22 | 857.45738 | 4.166667 |
| bgvar | 2016-09-23 | 711.14236 | 0.000000 |
| bgvar | 2016-09-24 | 260.44261 | 0.000000 |
| bgvar | 2016-09-25 | 184.80145 | 24.137931 |
| bgvar | 2016-09-26 | 440.85094 | 23.972603 |
| bgvar | 2016-09-27 | 491.20719 | 0.000000 |
| bgvar | 2016-09-28 | 377.75735 | 0.000000 |
| bgvar | 2016-09-29 | 314.93761 | 0.000000 |
| bgvar | 2016-09-30 | 139.47432 | 0.000000 |
| bgvar | 2016-10-01 | 150.01555 | 0.000000 |
| bgvar | 2016-10-02 | 277.74222 | 0.000000 |
| bgvar | 2016-10-03 | 454.50341 | 0.000000 |
| bgvar | 2016-10-04 | 56.91992 | 62.000000 |
| bgvar | 2016-10-05 | 172.40663 | 0.000000 |
| bgvar | 2016-10-06 | 78.29705 | 0.000000 |
| bgvar | 2016-10-07 | 41.57537 | 80.263158 |
| bgvar | 2016-10-08 | 783.12935 | 0.000000 |
| ctcdv | 2016-09-19 | 50.30023 | 0.000000 |
| ctcdv | 2016-09-20 | 85.42252 | 0.000000 |
| ctcdv | 2016-09-21 | 130.10560 | 0.000000 |
| ctcdv | 2016-09-22 | 54.71262 | 0.000000 |
| ctcdv | 2016-09-23 | 52.13749 | 26.250000 |
| ctcdv | 2016-09-24 | 28.15540 | 0.000000 |
| ctcdv | 2016-09-25 | 10.55126 | 23.140496 |
| ctcdv | 2016-09-26 | 54.20148 | 0.000000 |
| ctcdv | 2016-09-27 | 100.29212 | 5.327869 |
| ctcdv | 2016-09-28 | 96.27593 | 0.000000 |
| ctcdv | 2016-09-29 | 19.32725 | 0.000000 |
| ctcdv | 2016-09-30 | 11.10501 | 0.000000 |
| ctcdv | 2016-10-01 | 16.55122 | 6.882591 |
| ctcdv | 2016-10-02 | 20.20902 | 0.000000 |
| ctcdv | 2016-10-03 | 176.94452 | 0.000000 |
| ctcdv | 2016-10-04 | 142.89984 | 0.000000 |
| ctcdv | 2016-10-05 | 78.40051 | 15.537849 |
| ctcdv | 2016-10-06 | 61.21737 | 10.714286 |
| ctcdv | 2016-10-07 | 126.07335 | 0.000000 |
| ctcdv | 2016-10-08 | 75.33061 | 0.000000 |
| ctpda | 2016-09-19 | 67.78757 | 0.000000 |
| ctpda | 2016-09-20 | 135.40669 | 0.000000 |
We also want MTR per hour (done in basically the same way as for the entire nights above):
#-Sum MTR with in time (over heights), create Hour from Time and then average over Hour-
flyway_night %>%
group_by(radar_id, date_of_sunset, Time) %>% # For each site, night and timestamp:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE)) %>% #Sum MTR over all heights
mutate(Hour=substring(Time, 1, 2)) %>% #Create "hour" from "Time"
group_by(radar_id, date_of_sunset, Hour)%>% #For each site, night, and hour:
summarize(
average_MTR = mean(sum_MTR, na.rm = TRUE) #Average MTR (per hour)
)-> hourly_MTR
Quick look at hourly_MTR, 1 to 100:
| radar_id | date_of_sunset | Hour | average_MTR |
|---|---|---|---|
| bejab | 2016-09-19 | 00 | 134.6938463 |
| bejab | 2016-09-19 | 01 | 89.9535232 |
| bejab | 2016-09-19 | 02 | 21.3934127 |
| bejab | 2016-09-19 | 03 | 0.0000000 |
| bejab | 2016-09-19 | 04 | 0.0000000 |
| bejab | 2016-09-19 | 05 | 0.0000000 |
| bejab | 2016-09-19 | 17 | 0.0891750 |
| bejab | 2016-09-19 | 18 | 12.1469442 |
| bejab | 2016-09-19 | 19 | 98.1047580 |
| bejab | 2016-09-19 | 20 | 124.1766360 |
| bejab | 2016-09-19 | 21 | 186.7662186 |
| bejab | 2016-09-19 | 22 | 176.1024486 |
| bejab | 2016-09-19 | 23 | 162.8425719 |
| bejab | 2016-09-20 | 00 | 47.9264822 |
| bejab | 2016-09-20 | 01 | 38.0499119 |
| bejab | 2016-09-20 | 02 | 38.3341032 |
| bejab | 2016-09-20 | 03 | 28.9646748 |
| bejab | 2016-09-20 | 04 | 10.7764200 |
| bejab | 2016-09-20 | 05 | 5.9514906 |
| bejab | 2016-09-20 | 17 | 16.5586724 |
| bejab | 2016-09-20 | 18 | 23.8283841 |
| bejab | 2016-09-20 | 19 | 156.7883394 |
| bejab | 2016-09-20 | 20 | 149.6928159 |
| bejab | 2016-09-20 | 21 | 146.9910414 |
| bejab | 2016-09-20 | 22 | 101.4411203 |
| bejab | 2016-09-20 | 23 | 72.3926074 |
| bejab | 2016-09-21 | 00 | 56.7541136 |
| bejab | 2016-09-21 | 01 | 54.0603456 |
| bejab | 2016-09-21 | 02 | 38.4730769 |
| bejab | 2016-09-21 | 03 | 16.7617126 |
| bejab | 2016-09-21 | 04 | 9.0657699 |
| bejab | 2016-09-21 | 05 | 7.1119878 |
| bejab | 2016-09-21 | 17 | 13.4562295 |
| bejab | 2016-09-21 | 18 | 12.3627290 |
| bejab | 2016-09-21 | 19 | 79.1912637 |
| bejab | 2016-09-21 | 20 | 72.1154015 |
| bejab | 2016-09-21 | 21 | 72.3589724 |
| bejab | 2016-09-21 | 22 | 85.9015877 |
| bejab | 2016-09-21 | 23 | 89.5547313 |
| bejab | 2016-09-22 | 00 | 52.2775055 |
| bejab | 2016-09-22 | 01 | 38.2469535 |
| bejab | 2016-09-22 | 02 | 39.5313797 |
| bejab | 2016-09-22 | 03 | 47.1009477 |
| bejab | 2016-09-22 | 04 | 34.4885463 |
| bejab | 2016-09-22 | 17 | 10.1196727 |
| bejab | 2016-09-22 | 18 | 20.8571081 |
| bejab | 2016-09-22 | 19 | 77.0783823 |
| bejab | 2016-09-22 | 20 | 54.2286454 |
| bejab | 2016-09-22 | 21 | 44.8206987 |
| bejab | 2016-09-22 | 22 | 48.2684418 |
| bejab | 2016-09-22 | 23 | 54.2234761 |
| bejab | 2016-09-23 | 00 | 52.7981591 |
| bejab | 2016-09-23 | 01 | 53.9565404 |
| bejab | 2016-09-23 | 02 | 30.2344071 |
| bejab | 2016-09-23 | 03 | 17.7076176 |
| bejab | 2016-09-23 | 04 | 12.0682597 |
| bejab | 2016-09-23 | 05 | 10.8917907 |
| bejab | 2016-09-23 | 17 | 0.2517249 |
| bejab | 2016-09-23 | 18 | 15.6969478 |
| bejab | 2016-09-23 | 19 | 71.5993148 |
| bejab | 2016-09-23 | 20 | 83.4951128 |
| bejab | 2016-09-23 | 21 | 105.2335325 |
| bejab | 2016-09-23 | 22 | 96.2125233 |
| bejab | 2016-09-23 | 23 | 54.6509165 |
| bejab | 2016-09-24 | 00 | 18.7449478 |
| bejab | 2016-09-24 | 01 | 14.5077804 |
| bejab | 2016-09-24 | 02 | 11.8255138 |
| bejab | 2016-09-24 | 03 | 6.0151946 |
| bejab | 2016-09-24 | 04 | 4.2599895 |
| bejab | 2016-09-24 | 05 | 4.5661667 |
| bejab | 2016-09-24 | 17 | 45.2489457 |
| bejab | 2016-09-24 | 18 | 52.1222186 |
| bejab | 2016-09-24 | 19 | 52.6198350 |
| bejab | 2016-09-24 | 20 | 41.6706159 |
| bejab | 2016-09-24 | 21 | 40.0506770 |
| bejab | 2016-09-24 | 22 | 34.3277114 |
| bejab | 2016-09-24 | 23 | 23.8929051 |
| bejab | 2016-09-25 | 00 | 23.4823846 |
| bejab | 2016-09-25 | 01 | 20.5916497 |
| bejab | 2016-09-25 | 02 | 25.2650170 |
| bejab | 2016-09-25 | 03 | 17.8844855 |
| bejab | 2016-09-25 | 04 | 16.8685138 |
| bejab | 2016-09-25 | 05 | 7.2351049 |
| bejab | 2016-09-25 | 17 | 18.2507964 |
| bejab | 2016-09-25 | 18 | 47.9368448 |
| bejab | 2016-09-25 | 19 | 64.9586211 |
| bejab | 2016-09-25 | 20 | 28.3091404 |
| bejab | 2016-09-25 | 21 | 34.3379980 |
| bejab | 2016-09-25 | 22 | 49.8179165 |
| bejab | 2016-09-25 | 23 | 42.5924683 |
| bejab | 2016-09-26 | 00 | 187.6884758 |
| bejab | 2016-09-26 | 01 | 206.5370058 |
| bejab | 2016-09-26 | 02 | 155.2916401 |
| bejab | 2016-09-26 | 03 | 97.4857844 |
| bejab | 2016-09-26 | 04 | 62.5704133 |
| bejab | 2016-09-26 | 05 | 19.3788821 |
| bejab | 2016-09-26 | 17 | 0.1073127 |
| bejab | 2016-09-26 | 18 | 16.4223928 |
| bejab | 2016-09-26 | 19 | 56.7187528 |
| bejab | 2016-09-26 | 20 | 48.7734870 |
Plot MTR
A quick look at mean MTR per night for all radar stations:
Plot direction against speed
As a way of checking the data, plot directions against speed in scatter plot, filtered for only dens above 5. For most countries it looks reasonable, with high speeds in the seasonally appropriate direction. The spread of speeds is probably higher than we would expect, with plenty of speeds around 0 and all the way up to 30 (for some outlines 40). But this is for every measurement, which might cause some additional variation. Note that there is someting wrong with FI, and maybe also CZ. This is probably caused by problems with the dealising.
To take away some of the variation, we can plot the same thing with means per night instead:
MTR over season
To see the seasonal pattern at each site, we calculate how large percentage of the total MTR (sum of all means) each night contributes. One thing we discussed during the flyway-workshop was over how many nights the bulk of the migration passes a site. This makes a little less sense now that we have only a short time window and not the entire season, but lets calculate the cumulative percentages anyway.
So, for each site: calculate the percent of the total MTR that that night contributes, and then the cumulative percent (and number the nights in descending order):
flyway_agg_mtr %>%
group_by(radar_id) %>% #For each radar:
mutate(total_sum_MTR=sum(mean_MTR))%>% #Calculate total sum of mean MTRs per night
mutate(percent_MTR=(mean_MTR/total_sum_MTR)*100)%>% #Divide each night by the total sum, take times 100
arrange(radar_id, desc(percent_MTR))%>% #Order by descending percentage
mutate(cuml_percent=cumsum(percent_MTR))%>% #Calculate cumulative sum of percentage
mutate(n_cuml = row_number() #Number nights (1=night w the greatest contribution, ect)
)-> Cumul_mtr
Quick look at Cumul_mtr, 1 to 100:
| radar_id | date_of_sunset | mean_MTR | percent_rain | total_sum_MTR | percent_MTR | cuml_percent | n_cuml | latitude | longitude |
|---|---|---|---|---|---|---|---|---|---|
| bejab | 2016-10-04 | 1140.40112 | 0.000000 | 4386.115 | 26.0002557 | 26.00026 | 1 | 51.19170 | 3.06420 |
| bejab | 2016-10-03 | 1025.04929 | 0.000000 | 4386.115 | 23.3703239 | 49.37058 | 2 | 51.19170 | 3.06420 |
| bejab | 2016-10-05 | 478.64720 | 0.000000 | 4386.115 | 10.9127826 | 60.28336 | 3 | 51.19170 | 3.06420 |
| bejab | 2016-10-08 | 326.43206 | 33.566434 | 4386.115 | 7.4423963 | 67.72576 | 4 | 51.19170 | 3.06420 |
| bejab | 2016-10-06 | 265.00750 | 0.000000 | 4386.115 | 6.0419643 | 73.76772 | 5 | 51.19170 | 3.06420 |
| bejab | 2016-10-07 | 255.11656 | 0.000000 | 4386.115 | 5.8164586 | 79.58418 | 6 | 51.19170 | 3.06420 |
| bejab | 2016-09-27 | 128.64264 | 0.000000 | 4386.115 | 2.9329517 | 82.51713 | 7 | 51.19170 | 3.06420 |
| bejab | 2016-09-26 | 92.81671 | 0.000000 | 4386.115 | 2.1161486 | 84.63328 | 8 | 51.19170 | 3.06420 |
| bejab | 2016-10-02 | 91.71400 | 11.510791 | 4386.115 | 2.0910077 | 86.72429 | 9 | 51.19170 | 3.06420 |
| bejab | 2016-09-19 | 85.13902 | 55.384615 | 4386.115 | 1.9411033 | 88.66539 | 10 | 51.19170 | 3.06420 |
| bejab | 2016-09-28 | 75.17468 | 0.000000 | 4386.115 | 1.7139241 | 90.37932 | 11 | 51.19170 | 3.06420 |
| bejab | 2016-09-29 | 74.10825 | 8.029197 | 4386.115 | 1.6896103 | 92.06893 | 12 | 51.19170 | 3.06420 |
| bejab | 2016-09-20 | 69.47089 | 0.000000 | 4386.115 | 1.5838821 | 93.65281 | 13 | 51.19170 | 3.06420 |
| bejab | 2016-09-21 | 49.89622 | 0.000000 | 4386.115 | 1.1375949 | 94.79040 | 14 | 51.19170 | 3.06420 |
| bejab | 2016-09-23 | 49.84186 | 0.000000 | 4386.115 | 1.1363556 | 95.92676 | 15 | 51.19170 | 3.06420 |
| bejab | 2016-09-22 | 46.88227 | 14.159292 | 4386.115 | 1.0688792 | 96.99564 | 16 | 51.19170 | 3.06420 |
| bejab | 2016-09-30 | 41.30632 | 23.188406 | 4386.115 | 0.9417518 | 97.93739 | 17 | 51.19170 | 3.06420 |
| bejab | 2016-10-01 | 32.65174 | 75.000000 | 4386.115 | 0.7444343 | 98.68183 | 18 | 51.19170 | 3.06420 |
| bejab | 2016-09-25 | 31.67191 | 0.000000 | 4386.115 | 0.7220948 | 99.40392 | 19 | 51.19170 | 3.06420 |
| bejab | 2016-09-24 | 26.14476 | 0.000000 | 4386.115 | 0.5960802 | 100.00000 | 20 | 51.19170 | 3.06420 |
| bewid | 2016-10-03 | 7254.63828 | 0.000000 | 20028.529 | 36.2215235 | 36.22152 | 1 | 49.91430 | 5.50560 |
| bewid | 2016-10-04 | 3657.00468 | 0.000000 | 20028.529 | 18.2589780 | 54.48050 | 2 | 49.91430 | 5.50560 |
| bewid | 2016-09-21 | 2371.76064 | 0.000000 | 20028.529 | 11.8419114 | 66.32241 | 3 | 49.91430 | 5.50560 |
| bewid | 2016-10-05 | 1364.67679 | 0.000000 | 20028.529 | 6.8136647 | 73.13608 | 4 | 49.91430 | 5.50560 |
| bewid | 2016-09-26 | 1331.49167 | 5.185185 | 20028.529 | 6.6479754 | 79.78405 | 5 | 49.91430 | 5.50560 |
| bewid | 2016-10-08 | 1329.47220 | 0.000000 | 20028.529 | 6.6378924 | 86.42195 | 6 | 49.91430 | 5.50560 |
| bewid | 2016-09-22 | 680.98369 | 0.000000 | 20028.529 | 3.4000685 | 89.82201 | 7 | 49.91430 | 5.50560 |
| bewid | 2016-10-07 | 619.64518 | 1.398601 | 20028.529 | 3.0938127 | 92.91583 | 8 | 49.91430 | 5.50560 |
| bewid | 2016-09-25 | 295.97874 | 0.000000 | 20028.529 | 1.4777857 | 94.39361 | 9 | 49.91430 | 5.50560 |
| bewid | 2016-09-23 | 275.52608 | 0.000000 | 20028.529 | 1.3756681 | 95.76928 | 10 | 49.91430 | 5.50560 |
| bewid | 2016-10-02 | 266.53593 | 8.633093 | 20028.529 | 1.3307814 | 97.10006 | 11 | 49.91430 | 5.50560 |
| bewid | 2016-09-27 | 243.91098 | 0.000000 | 20028.529 | 1.2178178 | 98.31788 | 12 | 49.91430 | 5.50560 |
| bewid | 2016-10-06 | 108.90162 | 0.000000 | 20028.529 | 0.5437325 | 98.86161 | 13 | 49.91430 | 5.50560 |
| bewid | 2016-09-24 | 78.39796 | 0.000000 | 20028.529 | 0.3914314 | 99.25304 | 14 | 49.91430 | 5.50560 |
| bewid | 2016-09-29 | 64.84973 | 2.919708 | 20028.529 | 0.3237868 | 99.57683 | 15 | 49.91430 | 5.50560 |
| bewid | 2016-09-28 | 39.12097 | 0.000000 | 20028.529 | 0.1953262 | 99.77216 | 16 | 49.91430 | 5.50560 |
| bewid | 2016-10-01 | 34.54364 | 44.927536 | 20028.529 | 0.1724722 | 99.94463 | 17 | 49.91430 | 5.50560 |
| bewid | 2016-09-30 | 11.09008 | 92.753623 | 20028.529 | 0.0553714 | 100.00000 | 18 | 49.91430 | 5.50560 |
| bezav | 2016-10-03 | 1908.87124 | 0.000000 | 5126.566 | 37.2348926 | 37.23489 | 1 | 50.90550 | 4.45500 |
| bezav | 2016-10-04 | 1210.42543 | 0.000000 | 5126.566 | 23.6108439 | 60.84574 | 2 | 50.90550 | 4.45500 |
| bezav | 2016-10-05 | 343.67656 | 0.000000 | 5126.566 | 6.7038361 | 67.54957 | 3 | 50.90550 | 4.45500 |
| bezav | 2016-10-08 | 283.07568 | 0.000000 | 5126.566 | 5.5217409 | 73.07131 | 4 | 50.90550 | 4.45500 |
| bezav | 2016-09-26 | 224.63421 | 0.000000 | 5126.566 | 4.3817678 | 77.45308 | 5 | 50.90550 | 4.45500 |
| bezav | 2016-10-07 | 190.29835 | 0.000000 | 5126.566 | 3.7120046 | 81.16509 | 6 | 50.90550 | 4.45500 |
| bezav | 2016-10-02 | 171.66247 | 7.857143 | 5126.566 | 3.3484885 | 84.51357 | 7 | 50.90550 | 4.45500 |
| bezav | 2016-09-20 | 157.68062 | 0.000000 | 5126.566 | 3.0757554 | 87.58933 | 8 | 50.90550 | 4.45500 |
| bezav | 2016-09-21 | 103.50118 | 0.000000 | 5126.566 | 2.0189185 | 89.60825 | 9 | 50.90550 | 4.45500 |
| bezav | 2016-09-19 | 101.50319 | 0.000000 | 5126.566 | 1.9799451 | 91.58819 | 10 | 50.90550 | 4.45500 |
| bezav | 2016-10-06 | 70.06894 | 0.000000 | 5126.566 | 1.3667813 | 92.95497 | 11 | 50.90550 | 4.45500 |
| bezav | 2016-09-22 | 62.61783 | 0.000000 | 5126.566 | 1.2214381 | 94.17641 | 12 | 50.90550 | 4.45500 |
| bezav | 2016-09-29 | 53.16129 | 1.550388 | 5126.566 | 1.0369766 | 95.21339 | 13 | 50.90550 | 4.45500 |
| bezav | 2016-09-23 | 50.65719 | 0.000000 | 5126.566 | 0.9881311 | 96.20152 | 14 | 50.90550 | 4.45500 |
| bezav | 2016-09-24 | 37.29761 | 0.000000 | 5126.566 | 0.7275360 | 96.92906 | 15 | 50.90550 | 4.45500 |
| bezav | 2016-09-27 | 35.99292 | 0.000000 | 5126.566 | 0.7020863 | 97.63114 | 16 | 50.90550 | 4.45500 |
| bezav | 2016-09-25 | 35.75472 | 0.000000 | 5126.566 | 0.6974400 | 98.32858 | 17 | 50.90550 | 4.45500 |
| bezav | 2016-09-28 | 32.47516 | 0.000000 | 5126.566 | 0.6334681 | 98.96205 | 18 | 50.90550 | 4.45500 |
| bezav | 2016-09-30 | 29.78694 | 36.231884 | 5126.566 | 0.5810312 | 99.54308 | 19 | 50.90550 | 4.45500 |
| bezav | 2016-10-01 | 23.42420 | 16.071429 | 5126.566 | 0.4569180 | 100.00000 | 20 | 50.90550 | 4.45500 |
| bgvar | 2016-09-22 | 857.45738 | 4.166667 | 6747.977 | 12.7068805 | 12.70688 | 1 | 43.27694 | 27.79750 |
| bgvar | 2016-10-08 | 783.12935 | 0.000000 | 6747.977 | 11.6053945 | 24.31227 | 2 | 43.27694 | 27.79750 |
| bgvar | 2016-09-23 | 711.14236 | 0.000000 | 6747.977 | 10.5386008 | 34.85088 | 3 | 43.27694 | 27.79750 |
| bgvar | 2016-09-27 | 491.20719 | 0.000000 | 6747.977 | 7.2793252 | 42.13020 | 4 | 43.27694 | 27.79750 |
| bgvar | 2016-10-03 | 454.50341 | 0.000000 | 6747.977 | 6.7354025 | 48.86560 | 5 | 43.27694 | 27.79750 |
| bgvar | 2016-09-26 | 440.85094 | 23.972603 | 6747.977 | 6.5330829 | 55.39869 | 6 | 43.27694 | 27.79750 |
| bgvar | 2016-09-28 | 377.75735 | 0.000000 | 6747.977 | 5.5980830 | 60.99677 | 7 | 43.27694 | 27.79750 |
| bgvar | 2016-09-20 | 347.65164 | 26.056338 | 6747.977 | 5.1519387 | 66.14871 | 8 | 43.27694 | 27.79750 |
| bgvar | 2016-09-21 | 317.36329 | 9.090909 | 6747.977 | 4.7030878 | 70.85180 | 9 | 43.27694 | 27.79750 |
| bgvar | 2016-09-29 | 314.93761 | 0.000000 | 6747.977 | 4.6671412 | 75.51894 | 10 | 43.27694 | 27.79750 |
| bgvar | 2016-09-19 | 290.30139 | 16.197183 | 6747.977 | 4.3020507 | 79.82099 | 11 | 43.27694 | 27.79750 |
| bgvar | 2016-10-02 | 277.74222 | 0.000000 | 6747.977 | 4.1159330 | 83.93692 | 12 | 43.27694 | 27.79750 |
| bgvar | 2016-09-24 | 260.44261 | 0.000000 | 6747.977 | 3.8595657 | 87.79649 | 13 | 43.27694 | 27.79750 |
| bgvar | 2016-09-25 | 184.80145 | 24.137931 | 6747.977 | 2.7386200 | 90.53511 | 14 | 43.27694 | 27.79750 |
| bgvar | 2016-10-05 | 172.40663 | 0.000000 | 6747.977 | 2.5549380 | 93.09004 | 15 | 43.27694 | 27.79750 |
| bgvar | 2016-10-01 | 150.01555 | 0.000000 | 6747.977 | 2.2231189 | 95.31316 | 16 | 43.27694 | 27.79750 |
| bgvar | 2016-09-30 | 139.47432 | 0.000000 | 6747.977 | 2.0669056 | 97.38007 | 17 | 43.27694 | 27.79750 |
| bgvar | 2016-10-06 | 78.29705 | 0.000000 | 6747.977 | 1.1603041 | 98.54037 | 18 | 43.27694 | 27.79750 |
| bgvar | 2016-10-04 | 56.91992 | 62.000000 | 6747.977 | 0.8435108 | 99.38388 | 19 | 43.27694 | 27.79750 |
| bgvar | 2016-10-07 | 41.57537 | 80.263158 | 6747.977 | 0.6161161 | 100.00000 | 20 | 43.27694 | 27.79750 |
| ctcdv | 2016-10-03 | 176.94452 | 0.000000 | 1390.213 | 12.7278676 | 12.72787 | 1 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-04 | 142.89984 | 0.000000 | 1390.213 | 10.2789866 | 23.00685 | 2 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-21 | 130.10560 | 0.000000 | 1390.213 | 9.3586783 | 32.36553 | 3 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-07 | 126.07335 | 0.000000 | 1390.213 | 9.0686334 | 41.43417 | 4 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-27 | 100.29212 | 5.327869 | 1390.213 | 7.2141533 | 48.64832 | 5 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-28 | 96.27593 | 0.000000 | 1390.213 | 6.9252631 | 55.57358 | 6 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-20 | 85.42252 | 0.000000 | 1390.213 | 6.1445622 | 61.71814 | 7 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-05 | 78.40051 | 15.537849 | 1390.213 | 5.6394590 | 67.35760 | 8 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-08 | 75.33061 | 0.000000 | 1390.213 | 5.4186367 | 72.77624 | 9 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-06 | 61.21737 | 10.714286 | 1390.213 | 4.4034516 | 77.17969 | 10 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-22 | 54.71262 | 0.000000 | 1390.213 | 3.9355553 | 81.11525 | 11 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-26 | 54.20148 | 0.000000 | 1390.213 | 3.8987888 | 85.01404 | 12 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-23 | 52.13749 | 26.250000 | 1390.213 | 3.7503230 | 88.76436 | 13 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-19 | 50.30023 | 0.000000 | 1390.213 | 3.6181662 | 92.38253 | 14 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-24 | 28.15540 | 0.000000 | 1390.213 | 2.0252572 | 94.40778 | 15 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-02 | 20.20902 | 0.000000 | 1390.213 | 1.4536629 | 95.86145 | 16 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-29 | 19.32725 | 0.000000 | 1390.213 | 1.3902363 | 97.25168 | 17 | 41.60192 | 1.40283 |
| ctcdv | 2016-10-01 | 16.55122 | 6.882591 | 1390.213 | 1.1905529 | 98.44223 | 18 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-30 | 11.10501 | 0.000000 | 1390.213 | 0.7987987 | 99.24103 | 19 | 41.60192 | 1.40283 |
| ctcdv | 2016-09-25 | 10.55126 | 23.140496 | 1390.213 | 0.7589671 | 100.00000 | 20 | 41.60192 | 1.40283 |
| ctpda | 2016-09-20 | 135.40669 | 0.000000 | 1169.386 | 11.5792928 | 11.57929 | 1 | 41.88882 | 2.99717 |
| ctpda | 2016-10-04 | 135.15745 | 0.000000 | 1169.386 | 11.5579795 | 23.13727 | 2 | 41.88882 | 2.99717 |
Plot the total sum of MTR at each site to a map: 
Plot MTR percent
To see the seasonal pattern, we plot the pattern of intensity for each site over date and latitude. Size(area) of dots is relative to total MTR at that site and color opacity for each dot by procent MTR on that night. Illustrates how MTR increases with decreasing latitude, and (kind of) how the wave around oct 3 moves downwards.
Max MTR
To illustrate the wave clearer we calculate and plot the date of max MTR at each site. We then plot the date of max MTR against latitude. NOTE that this shows the max date within the specified timeframe, since some sites had single nights with high MTR also in beginning. We also filter away sites with a max MTR of zero, to exclude sites with 0 MTR for the given period (eg sease).
Find date of max MTR within given time frame:
#-Set timerange-
time_start<-"2016-09-28"
time_end<-"2016-10-08"
flyway_agg_mtr %>% #From the MTR per site and night:
filter(date_of_sunset > time_start &
date_of_sunset < time_end) %>% #Filter for time period above
group_by(radar_id) %>% #For each site:
filter(mean_MTR==max(mean_MTR)) %>% #Filter out night with max MTR
filter(mean_MTR > 0) %>% #Filter out sites with MTR=0
rename(date_of_max_MTR = `date_of_sunset`) %>% #Name the new variables
rename(max_MTR = `mean_MTR`
)-> max_MTR_temp
Plot the date of maximum MTR between 2016-09-28 and 2016-10-08 against latitude. Color and size of dots by the MTR at that site on that date:
Plot max date of MTR
Plot a map of the sites with each max date of MTR (NOTE! only with in time limit set above) and the centriod, (mean coordinates for all sites that have that day as max). So, each site is only included once, and is plotted to the date when its MTR was highest within the given time limits.
Plot cumulative MTR
Plot cumulative percentages of all sites in plot, divided into three latitude groups; South < 50, Middle 50-60 and North > 60. Black lines show how many days it takes to build up 50% and 90% of total MTR at the site. So, for almost all sites half of the total MTR of the period passes in the 5 peak nights. There does not seem to be an obvious difference between latitudes.
Cumulative MTR within night.
Calculate how much each hour contributes to nightly total (percent) and the cumulative percentage during each night.
hourly_MTR %>%
group_by(radar_id, date_of_sunset) %>% #For each site and night:
mutate(night_sum_mtr=sum(average_MTR))%>% #Calculate nightly sum MTR
mutate(percent_MTR=(average_MTR/night_sum_mtr)*100)%>% #Percent of total night for each hour
arrange(radar_id, date_of_sunset, desc(percent_MTR))%>% #Arrange in decending order
mutate(cuml_percent=cumsum(percent_MTR))%>% #Calculate cumulative percent
mutate(n_cuml = row_number() #Number hours(1=h with the greatest contribution,so on...)
)-> Cumul_MTR_hour
Quick look at Cumul_mtr_hour, 1 to 100:
| radar_id | date_of_sunset | Hour | average_MTR | night_sum_mtr | percent_MTR | cuml_percent | n_cuml | latitude | longitude | lat_group |
|---|---|---|---|---|---|---|---|---|---|---|
| bejab | 2016-09-19 | 21 | 186.7662186 | 1006.2695 | 18.5602577 | 18.56026 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 22 | 176.1024486 | 1006.2695 | 17.5005247 | 36.06078 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 23 | 162.8425719 | 1006.2695 | 16.1827986 | 52.24358 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 00 | 134.6938463 | 1006.2695 | 13.3854640 | 65.62905 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 20 | 124.1766360 | 1006.2695 | 12.3402957 | 77.96934 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 19 | 98.1047580 | 1006.2695 | 9.7493519 | 87.71869 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 01 | 89.9535232 | 1006.2695 | 8.9393070 | 96.65800 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 02 | 21.3934127 | 1006.2695 | 2.1260122 | 98.78401 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 18 | 12.1469442 | 1006.2695 | 1.2071263 | 99.99114 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 17 | 0.0891750 | 1006.2695 | 0.0088619 | 100.00000 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 03 | 0.0000000 | 1006.2695 | 0.0000000 | 100.00000 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 04 | 0.0000000 | 1006.2695 | 0.0000000 | 100.00000 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-19 | 05 | 0.0000000 | 1006.2695 | 0.0000000 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 19 | 156.7883394 | 837.6961 | 18.7166141 | 18.71661 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 20 | 149.6928159 | 837.6961 | 17.8695857 | 36.58620 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 21 | 146.9910414 | 837.6961 | 17.5470613 | 54.13326 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 22 | 101.4411203 | 837.6961 | 12.1095377 | 66.24280 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 23 | 72.3926074 | 837.6961 | 8.6418703 | 74.88467 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 00 | 47.9264822 | 837.6961 | 5.7212257 | 80.60589 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 02 | 38.3341032 | 837.6961 | 4.5761350 | 85.18203 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 01 | 38.0499119 | 837.6961 | 4.5422097 | 89.72424 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 03 | 28.9646748 | 837.6961 | 3.4576592 | 93.18190 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 18 | 23.8283841 | 837.6961 | 2.8445143 | 96.02641 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 17 | 16.5586724 | 837.6961 | 1.9766922 | 98.00311 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 04 | 10.7764200 | 837.6961 | 1.2864356 | 99.28954 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-20 | 05 | 5.9514906 | 837.6961 | 0.7104594 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 23 | 89.5547313 | 607.1679 | 14.7495821 | 14.74958 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 22 | 85.9015877 | 607.1679 | 14.1479127 | 28.89749 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 19 | 79.1912637 | 607.1679 | 13.0427285 | 41.94022 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 21 | 72.3589724 | 607.1679 | 11.9174564 | 53.85768 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 20 | 72.1154015 | 607.1679 | 11.8773405 | 65.73502 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 00 | 56.7541136 | 607.1679 | 9.3473505 | 75.08237 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 01 | 54.0603456 | 607.1679 | 8.9036894 | 83.98606 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 02 | 38.4730769 | 607.1679 | 6.3364805 | 90.32254 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 03 | 16.7617126 | 607.1679 | 2.7606387 | 93.08318 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 17 | 13.4562295 | 607.1679 | 2.2162287 | 95.29941 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 18 | 12.3627290 | 607.1679 | 2.0361301 | 97.33554 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 04 | 9.0657699 | 607.1679 | 1.4931240 | 98.82866 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-21 | 05 | 7.1119878 | 607.1679 | 1.1713379 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 19 | 77.0783823 | 521.2418 | 14.7874535 | 14.78745 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 20 | 54.2286454 | 521.2418 | 10.4037416 | 25.19120 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 23 | 54.2234761 | 521.2418 | 10.4027498 | 35.59394 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 00 | 52.2775055 | 521.2418 | 10.0294162 | 45.62336 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 22 | 48.2684418 | 521.2418 | 9.2602792 | 54.88364 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 03 | 47.1009477 | 521.2418 | 9.0362959 | 63.91994 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 21 | 44.8206987 | 521.2418 | 8.5988312 | 72.51877 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 02 | 39.5313797 | 521.2418 | 7.5840777 | 80.10285 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 01 | 38.2469535 | 521.2418 | 7.3376611 | 87.44051 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 04 | 34.4885463 | 521.2418 | 6.6166123 | 94.05712 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 18 | 20.8571081 | 521.2418 | 4.0014269 | 98.05855 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-22 | 17 | 10.1196727 | 521.2418 | 1.9414547 | 100.00000 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 21 | 105.2335325 | 604.7968 | 17.3998150 | 17.39982 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 22 | 96.2125233 | 604.7968 | 15.9082382 | 33.30805 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 20 | 83.4951128 | 604.7968 | 13.8054808 | 47.11353 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 19 | 71.5993148 | 604.7968 | 11.8385728 | 58.95211 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 23 | 54.6509165 | 604.7968 | 9.0362436 | 67.98835 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 01 | 53.9565404 | 604.7968 | 8.9214322 | 76.90978 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 00 | 52.7981591 | 604.7968 | 8.7298999 | 85.63968 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 02 | 30.2344071 | 604.7968 | 4.9991013 | 90.63878 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 03 | 17.7076176 | 604.7968 | 2.9278621 | 93.56665 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 18 | 15.6969478 | 604.7968 | 2.5954083 | 96.16205 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 04 | 12.0682597 | 604.7968 | 1.9954237 | 98.15748 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 05 | 10.8917907 | 604.7968 | 1.8009007 | 99.95838 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-23 | 17 | 0.2517249 | 604.7968 | 0.0416214 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 19 | 52.6198350 | 349.8525 | 15.0405770 | 15.04058 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 18 | 52.1222186 | 349.8525 | 14.8983410 | 29.93892 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 17 | 45.2489457 | 349.8525 | 12.9337208 | 42.87264 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 20 | 41.6706159 | 349.8525 | 11.9109098 | 54.78355 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 21 | 40.0506770 | 349.8525 | 11.4478750 | 66.23142 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 22 | 34.3277114 | 349.8525 | 9.8120526 | 76.04348 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 23 | 23.8929051 | 349.8525 | 6.8294224 | 82.87290 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 00 | 18.7449478 | 349.8525 | 5.3579573 | 88.23086 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 01 | 14.5077804 | 349.8525 | 4.1468277 | 92.37768 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 02 | 11.8255138 | 349.8525 | 3.3801427 | 95.75783 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 03 | 6.0151946 | 349.8525 | 1.7193516 | 97.47718 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 05 | 4.5661667 | 349.8525 | 1.3051691 | 98.78235 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-24 | 04 | 4.2599895 | 349.8525 | 1.2176530 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 19 | 64.9586211 | 397.5309 | 16.3405195 | 16.34052 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 22 | 49.8179165 | 397.5309 | 12.5318337 | 28.87235 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 18 | 47.9368448 | 397.5309 | 12.0586450 | 40.93100 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 23 | 42.5924683 | 397.5309 | 10.7142524 | 51.64525 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 21 | 34.3379980 | 397.5309 | 8.6378177 | 60.28307 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 20 | 28.3091404 | 397.5309 | 7.1212420 | 67.40431 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 02 | 25.2650170 | 397.5309 | 6.3554844 | 73.75979 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 00 | 23.4823846 | 397.5309 | 5.9070583 | 79.66685 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 01 | 20.5916497 | 397.5309 | 5.1798860 | 84.84674 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 17 | 18.2507964 | 397.5309 | 4.5910380 | 89.43778 | 10 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 03 | 17.8844855 | 397.5309 | 4.4988914 | 93.93667 | 11 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 04 | 16.8685138 | 397.5309 | 4.2433210 | 98.17999 | 12 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-25 | 05 | 7.2351049 | 397.5309 | 1.8200105 | 100.00000 | 13 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 01 | 206.5370058 | 1140.9442 | 18.1022883 | 18.10229 | 1 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 00 | 187.6884758 | 1140.9442 | 16.4502767 | 34.55257 | 2 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 02 | 155.2916401 | 1140.9442 | 13.6108008 | 48.16337 | 3 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 23 | 133.9569707 | 1140.9442 | 11.7408873 | 59.90425 | 4 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 03 | 97.4857844 | 1140.9442 | 8.5443079 | 68.44856 | 5 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 22 | 95.4497203 | 1140.9442 | 8.3658536 | 76.81441 | 6 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 04 | 62.5704133 | 1140.9442 | 5.4840906 | 82.29851 | 7 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 21 | 60.5633466 | 1140.9442 | 5.3081779 | 87.60668 | 8 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 19 | 56.7187528 | 1140.9442 | 4.9712119 | 92.57790 | 9 | 51.1917 | 3.0642 | Middle50-60 |
| bejab | 2016-09-26 | 20 | 48.7734870 | 1140.9442 | 4.2748355 | 96.85273 | 10 | 51.1917 | 3.0642 | Middle50-60 |
Plot cumulative MTR per hour for each night to PDF
(let me know if you’re interested in seeing this): Nightly_cuml_MTR.pdf
MTR in realtion to sunset
To see when in relation to sunset the migratory activity kicks off, calculate and plot MTR (for all altitudes) against time difference (in minutes) from sunset.
Calculate time to sunset:
#-New dataframe (also including daytime) with MTR summed over all altitudes (per timestamp)
flyway_all %>%
mutate(date_of_sunset =
as.Date(as.character(date_of_sunset),'%Y%m%d')) %>% #Set "date_of_sunset" to date
mutate(datetime = as.POSIXct(
datetime, format="%Y-%m-%d %H:%M:%S"))%>% #Set "datetime" to date
filter(date_of_sunset != "2016-09-18" &
date_of_sunset != "2016-10-09")%>% #Filter out start and end dates
group_by(radar_id, datetime)%>% #For each site and timestamp:
summarize(
sum_mtr = sum(mtr, na.rm = TRUE) #sum MTR
)->flyway_sunset
#-Add lat and long from radar meta file-
flyway_sunset <-merge(flyway_sunset, radar_metadata[, c("radar_id","latitude", "longitude")], by = "radar_id")
#-Calc sunset time with biorad function suntime-
sunset<- suntime(flyway_sunset$longitude,flyway_sunset$latitude,flyway_sunset$datetime,rise=FALSE)
#-Add the sunset time to data frame-
flyway_sunset <-cbind(flyway_sunset, sunset)
#-Calculate the time diffrence, in minutes, between sunset and timestamp (datetime)-
sunset_diff <-as.numeric(difftime(flyway_sunset$datetime, flyway_sunset$sunset, units = "mins"))
#-Add time diffence to data frame-
flyway_sunset <-cbind(flyway_sunset, sunset_diff)
#-Select range around sunset to include (minutes)-
flyway_sunset%>%
filter(sunset_diff>-40, sunset_diff<120)->flyway_sunset_sel
#-Add latitude group-
flyway_sunset_sel$lat_group<-cut(flyway_sunset_sel$latitude, c(0,50,60,70), labels=c("South<50", "Middle50-60", "North>60"))
Take a look:
| radar_id | datetime | sum_mtr | latitude | longitude | sunset | sunset_diff | lat_group |
|---|---|---|---|---|---|---|---|
| bejab | 2016-09-19 17:10:00 | 7.1874553 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -39.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:15:00 | 7.1637843 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -34.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:20:00 | 7.5273640 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -29.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:25:00 | 7.8837885 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -24.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:30:00 | 4.9837060 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -19.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:35:00 | 9.6494861 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -14.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:40:00 | 9.5076675 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -9.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:45:00 | 0.5126445 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | -4.0455566 | Middle50-60 |
| bejab | 2016-09-19 17:50:00 | 0.0891750 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 0.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:00:00 | 0.4254417 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 10.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:05:00 | 0.0000000 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 15.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:10:00 | 0.2343906 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 20.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:15:00 | 0.4960401 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 25.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:20:00 | 0.5878986 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 30.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:25:00 | 1.5441202 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 35.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:30:00 | 1.0699600 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 40.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:35:00 | 1.7999739 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 45.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:40:00 | 9.2219277 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 50.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:45:00 | 50.2421276 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 55.9544434 | Middle50-60 |
| bejab | 2016-09-19 18:50:00 | 67.9945057 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 60.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:00:00 | 82.3582828 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 70.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:05:00 | 99.5158877 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 75.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:10:00 | 104.9864858 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 80.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:15:00 | 103.7023959 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 85.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:20:00 | 107.5241632 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 90.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:25:00 | 106.8154437 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 95.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:30:00 | 100.7949353 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 100.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:35:00 | 85.4996241 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 105.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:40:00 | 92.6349135 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 110.9544434 | Middle50-60 |
| bejab | 2016-09-19 19:45:00 | 97.2493911 | 51.1917 | 3.0642 | 2016-09-19 17:49:02 | 115.9544434 | Middle50-60 |
| bejab | 2016-09-20 17:10:00 | 13.2761481 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -36.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:15:00 | 19.1989421 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -31.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:20:00 | 10.1641707 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -26.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:25:00 | 10.6666907 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -21.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:30:00 | 12.2336496 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -16.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:35:00 | 15.5173043 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -11.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:40:00 | 9.4948928 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -6.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:45:00 | 12.1644924 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | -1.7572191 | Middle50-60 |
| bejab | 2016-09-20 17:50:00 | 16.5586724 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 3.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:00:00 | 17.5850691 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 13.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:05:00 | 0.2374115 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 18.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:10:00 | 1.1095932 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 23.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:15:00 | 0.3209132 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 28.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:20:00 | 0.2686283 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 33.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:25:00 | 0.2054739 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 38.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:30:00 | 0.1046943 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 43.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:35:00 | 10.4897783 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 48.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:40:00 | 59.7416787 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 53.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:45:00 | 85.2951929 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 58.2427809 | Middle50-60 |
| bejab | 2016-09-20 18:50:00 | 86.7537918 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 63.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:00:00 | 147.7151211 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 73.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:05:00 | 135.9079536 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 78.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:10:00 | 165.6650003 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 83.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:15:00 | 153.0963030 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 88.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:20:00 | 158.2885193 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 93.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:25:00 | 174.7880645 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 98.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:30:00 | 160.4748411 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 103.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:35:00 | 159.7697505 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 108.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:40:00 | 155.7379624 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 113.2427809 | Middle50-60 |
| bejab | 2016-09-20 19:45:00 | 153.4580498 | 51.1917 | 3.0642 | 2016-09-20 17:46:45 | 118.2427809 | Middle50-60 |
| bejab | 2016-09-21 17:05:00 | 4.1467945 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -39.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:10:00 | 0.1866060 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -34.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:15:00 | 0.0000000 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -29.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:20:00 | 0.2789369 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -24.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:25:00 | 7.4510231 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -19.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:30:00 | 0.8475255 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -14.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:35:00 | 8.0671440 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -9.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:40:00 | 10.5474771 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | -4.4689748 | Middle50-60 |
| bejab | 2016-09-21 17:45:00 | 14.9263930 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 0.5310252 | Middle50-60 |
| bejab | 2016-09-21 17:50:00 | 11.9860660 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 5.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:00:00 | 1.7818353 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 15.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:05:00 | 7.7921954 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 20.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:10:00 | 3.6142479 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 25.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:15:00 | 3.0516444 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 30.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:20:00 | 2.0854031 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 35.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:25:00 | 2.4222548 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 40.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:30:00 | 1.8707462 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 45.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:35:00 | 5.0880582 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 50.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:40:00 | 25.1338098 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 55.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:45:00 | 34.9864222 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 60.5310252 | Middle50-60 |
| bejab | 2016-09-21 18:50:00 | 48.1634012 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 65.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:00:00 | 79.6564669 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 75.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:05:00 | 77.0815750 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 80.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:10:00 | 73.3065315 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 85.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:15:00 | 74.6509092 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 90.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:20:00 | 88.9056135 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 95.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:25:00 | 84.7665383 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 100.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:30:00 | 80.5976325 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 105.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:35:00 | 80.6073214 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 110.5310252 | Middle50-60 |
| bejab | 2016-09-21 19:40:00 | 80.6328712 | 51.1917 | 3.0642 | 2016-09-21 17:44:28 | 115.5310252 | Middle50-60 |
| bejab | 2016-09-22 17:05:00 | 0.0000000 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -37.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:10:00 | 0.0000000 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -32.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:15:00 | 9.8068185 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -27.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:20:00 | 37.5540297 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -22.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:25:00 | 11.5885228 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -17.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:30:00 | 11.2342786 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -12.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:35:00 | 8.9113274 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -7.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:40:00 | 7.1576179 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | -2.1813170 | Middle50-60 |
| bejab | 2016-09-22 17:45:00 | 8.2014589 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | 2.8186830 | Middle50-60 |
| bejab | 2016-09-22 17:50:00 | 12.0378864 | 51.1917 | 3.0642 | 2016-09-22 17:42:10 | 7.8186830 | Middle50-60 |
Plot MTR in relation to sunset (with in the time window set above), faceted for latitude groups: NOTE, might need to adjust the scale of the plot to be clear. Maybe log?.
Calculate nr of birds
Needs to be checked if correct… Calculate the total number of birds passing (from the MTR). Done separately for the different time resolutions.
flyway_night %>%
filter(Country != "pl",
Country != "pt",
Country != "bg",
Country != "be",
Country != "ct", )%>% #Filter out Poland, Portugal, Bulgaria, Belgium, and catalonia
group_by(radar_id, date_of_sunset, Time) %>% #For each site, night and timestamp:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE)) %>% #Sum MTR
mutate(bird_per_hour=sum_MTR*0.25) %>% #Take times 0.25 (because 15min)
group_by(radar_id, date_of_sunset) %>% #For each site and night
summarize(
bird_nr = sum(bird_per_hour, na.rm = TRUE) #sum birds per hour
)-> bird_nr
flyway_night %>%
filter(Country == "pl"|
Country == "pt")%>% #Filter for only Poland and Portugal
group_by(radar_id, date_of_sunset, Time) %>% #For each site, night and timestamp:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE)) %>% #Sum MTR
mutate(bird_per_hour=sum_MTR*0.17) %>% #Take times 0.17 (because 10min)
group_by(radar_id, date_of_sunset) %>% #For each site and night
summarize(
bird_nr = sum(bird_per_hour, na.rm = TRUE) #sum birds per hour
)-> bird_nr_plpt
flyway_night %>%
filter(Country == "bg"|
Country == "be")%>% #Filter for only Bulgaria and Belgium
group_by(radar_id, date_of_sunset, Time) %>% #For each site, night and timestamp:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE)) %>% #Sum MTR
mutate(bird_per_hour=sum_MTR*0.08) %>% #Take times 0.08 (because 5min)
group_by(radar_id, date_of_sunset) %>% #For each site and night
summarize(
bird_nr = sum(bird_per_hour, na.rm = TRUE) #sum birds per hour
)-> bird_nr_bg_be
flyway_night %>%
filter(Country == "ct")%>% #Filter for only Catalonia
group_by(radar_id, date_of_sunset, Time) %>% #For each site, night and timestamp:
summarize(
sum_MTR = sum(mtr, na.rm = TRUE)) %>% #Sum MTR
mutate(bird_per_hour=sum_MTR*0.03) %>% #Take times 0.03 (because 2min)
group_by(radar_id, date_of_sunset) %>% #For each site and night
summarize(
bird_nr = sum(bird_per_hour, na.rm = TRUE) #sum birds per hour
)-> bird_nr_ct
#Add all together
bird_nr %>%
bind_rows(bird_nr_plpt, bird_nr_bg_be, bird_nr_ct
)->bird_nr
#-Total nr of birds passing each site-
bird_nr %>%
group_by(radar_id)%>% #For each site:
summarize(bird_nr=sum(bird_nr))%>% #Sum total nr of birds
mutate(country=substring(radar_id,1,2) #Add "country" from radar_id code
)-> total_bird
#-Add lat and lon from radar meta file-
total_bird <-merge(total_bird, radar_metadata[, c("radar_id","latitude", "longitude")], by = "radar_id")
In total, approximately 6 681 195 birds passed our 70 sites during these days (assuming a radar cross section of 11 cm^2).
Plot total nr of birds passing each site on map:
Plot total nr of birds at each site:
Mean flight directions
Aggregate directions to mean direction per night per site. Filter out points with a dens of less then 5, because not useful with mean dir if very low n. Also include only night. NOTE: now averages all heights and all times. Note also that by calculating nightly averages of direction like this we are exaggerating the variation as nights with very low densities (as long as some are over 5) weigh equally to high migration nights.
Circular mean and sd: Uses package “circular” to calculate mean, sd and rho. The mean then needs to be changed from a -180 to 180 scale back to 0 to 360. The sd is for some reason given in radians, so the deg is used to convert to sd in degrees.
flyway_night %>% #Start from the flyway all
filter(dens >5) %>% #Filter out all time stamps with dens less than 5
group_by(radar_id, date_of_sunset) %>% #For each site and night, calculate:
summarize(
avg_dd = mean.circular(
circular(dd, units = "degrees"), na.rm = TRUE), #Mean direction
n = length(dd), #Nr of datapoints
dd_sd = deg(sd.circular(
circular(dd, units = "degrees"), na.rm = TRUE)), #Mean SD
rho_dd = rho.circular(
circular(dd, units="degrees"), na.rm=TRUE), #R of mean dir
avg_ff = mean(ff, na.rm = TRUE)) %>% #Mean GS
mutate(avg_dd = ifelse(avg_dd < 0, (avg_dd+360), avg_dd))%>% #Change from -180 - 180, to: 0 - 360
mutate(country = substring(radar_id,1,2) #Add country
)->mean_dir
#-Add lat and lon, then wind, then mean MTR
mean_dir <-merge(mean_dir, radar_metadata[, c("radar_id","latitude", "longitude")], by = "radar_id")
mean_dir <-merge(mean_dir, wind, by = c("radar_id","date_of_sunset"))
mean_dir <-merge(mean_dir, flyway_agg_mtr[, c("radar_id","date_of_sunset", "mean_MTR")], by = c("radar_id", "date_of_sunset"))
Quick look at the mean dir:
| radar_id | date_of_sunset | avg_dd | n | dd_sd | rho_dd | avg_ff | country | latitude | longitude | index | wind_dir_to | wind.speed | mean_MTR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bejab | 2016-09-19 | 237.83867 | 232 | 17.136539 | 0.9562584 | 4.838267 | be | 51.19170 | 3.06420 | 2 | 225.0466 | 2.6848 | 85.139024 |
| bejab | 2016-09-20 | 256.87920 | 239 | 21.552577 | 0.9316953 | 3.668308 | be | 51.19170 | 3.06420 | 3 | 267.8200 | 2.9313 | 69.470889 |
| bejab | 2016-09-21 | 254.83352 | 308 | 62.031492 | 0.5565103 | 2.560880 | be | 51.19170 | 3.06420 | 4 | 8.7387 | 6.3777 | 49.896222 |
| bejab | 2016-09-22 | 95.65742 | 147 | 16.989626 | 0.9569889 | 3.959678 | be | 51.19170 | 3.06420 | 5 | 82.4233 | 5.5836 | 46.882273 |
| bejab | 2016-09-23 | 340.52309 | 157 | 74.499044 | 0.4294157 | 4.011670 | be | 51.19170 | 3.06420 | 6 | 27.8716 | 4.9125 | 49.841864 |
| bejab | 2016-09-24 | 356.77497 | 78 | 6.991977 | 0.9925816 | 5.744155 | be | 51.19170 | 3.06420 | 7 | 12.4516 | 12.2018 | 26.144764 |
| bejab | 2016-09-25 | 93.61510 | 117 | 14.650715 | 0.9678365 | 5.463813 | be | 51.19170 | 3.06420 | 8 | 95.5528 | 8.0744 | 31.671908 |
| bejab | 2016-09-26 | 260.35886 | 393 | 38.134644 | 0.8013198 | 4.154912 | be | 51.19170 | 3.06420 | 9 | 23.1111 | 3.7033 | 92.816711 |
| bejab | 2016-09-27 | 64.54204 | 130 | 5.908276 | 0.9946974 | 7.114847 | be | 51.19170 | 3.06420 | 10 | 71.6739 | 9.2837 | 128.642637 |
| bejab | 2016-09-28 | 63.81661 | 104 | 9.300692 | 0.9869113 | 10.192270 | be | 51.19170 | 3.06420 | 11 | 59.4755 | 13.1082 | 75.174683 |
| bejab | 2016-09-29 | 91.80234 | 126 | 10.721489 | 0.9826444 | 6.324291 | be | 51.19170 | 3.06420 | 12 | 89.7185 | 9.2973 | 74.108251 |
| bejab | 2016-09-30 | 14.52933 | 225 | 81.783683 | 0.3610541 | 2.670004 | be | 51.19170 | 3.06420 | 13 | 34.1124 | 5.6285 | 41.306317 |
| bejab | 2016-10-01 | 54.26861 | 22 | 15.959114 | 0.9619508 | 5.625941 | be | 51.19170 | 3.06420 | 14 | 100.2089 | 7.2921 | 32.651743 |
| bejab | 2016-10-02 | 167.71176 | 185 | 23.297569 | 0.9206553 | 6.739980 | be | 51.19170 | 3.06420 | 15 | 161.9992 | 5.9551 | 91.714001 |
| bejab | 2016-10-03 | 232.17707 | 749 | 7.946255 | 0.9904289 | 14.490525 | be | 51.19170 | 3.06420 | 16 | 257.9015 | 10.0079 | 1025.049286 |
| bejab | 2016-10-04 | 258.79927 | 326 | 18.171420 | 0.9509513 | 16.010272 | be | 51.19170 | 3.06420 | 17 | 283.2939 | 15.3036 | 1140.401116 |
| bejab | 2016-10-05 | 245.06340 | 339 | 12.583265 | 0.9761721 | 14.751319 | be | 51.19170 | 3.06420 | 18 | 261.9066 | 12.3681 | 478.647197 |
| bejab | 2016-10-06 | 247.50584 | 270 | 10.765890 | 0.9825017 | 11.096067 | be | 51.19170 | 3.06420 | 19 | 263.1453 | 9.4841 | 265.007501 |
| bejab | 2016-10-07 | 243.73263 | 361 | 15.245673 | 0.9652181 | 8.149820 | be | 51.19170 | 3.06420 | 20 | 241.8987 | 6.6234 | 255.116564 |
| bejab | 2016-10-08 | 231.19334 | 551 | 11.563391 | 0.9798405 | 7.242427 | be | 51.19170 | 3.06420 | 21 | 234.2281 | 4.9671 | 326.432060 |
| bewid | 2016-09-21 | 227.70861 | 1233 | 18.068974 | 0.9514892 | 8.135303 | be | 49.91430 | 5.50560 | 26 | 341.1655 | 3.1132 | 2371.760645 |
| bewid | 2016-09-22 | 204.47601 | 985 | 13.010822 | 0.9745465 | 5.524131 | be | 49.91430 | 5.50560 | 27 | 65.7085 | 4.7048 | 680.983693 |
| bewid | 2016-09-23 | 199.94824 | 667 | 16.852081 | 0.9576676 | 3.481475 | be | 49.91430 | 5.50560 | 28 | 49.1252 | 2.5227 | 275.526075 |
| bewid | 2016-09-24 | 348.20846 | 146 | 18.285858 | 0.9503472 | 8.796689 | be | 49.91430 | 5.50560 | 29 | 22.7189 | 9.9862 | 78.397957 |
| bewid | 2016-09-25 | 164.58911 | 504 | 16.982207 | 0.9570256 | 7.391380 | be | 49.91430 | 5.50560 | 30 | 115.2872 | 5.9186 | 295.978741 |
| bewid | 2016-09-26 | 215.38049 | 1048 | 19.132160 | 0.9457746 | 6.949398 | be | 49.91430 | 5.50560 | 31 | 35.1672 | 1.1840 | 1331.491665 |
| bewid | 2016-09-27 | 189.06370 | 694 | 42.658532 | 0.7579303 | 3.421069 | be | 49.91430 | 5.50560 | 32 | 64.4343 | 7.7547 | 243.910985 |
| bewid | 2016-09-28 | 49.62734 | 1 | 0.000000 | 1.0000000 | 2.545443 | be | 49.91430 | 5.50560 | 33 | 69.8711 | 9.8607 | 39.120974 |
| bewid | 2016-09-29 | 111.07179 | 93 | 19.048527 | 0.9462347 | 6.661824 | be | 49.91430 | 5.50560 | 34 | 77.2173 | 12.4171 | 64.849734 |
| bewid | 2016-09-30 | 29.55893 | 42 | 79.473960 | 0.3821284 | 2.801342 | be | 49.91430 | 5.50560 | 35 | 37.1700 | 6.0402 | 11.090078 |
| bewid | 2016-10-01 | 77.55348 | 32 | 2.681319 | 0.9989056 | 10.635730 | be | 49.91430 | 5.50560 | 36 | 79.5047 | 12.0172 | 34.543643 |
| bewid | 2016-10-02 | 153.07588 | 334 | 9.428520 | 0.9865515 | 12.236624 | be | 49.91430 | 5.50560 | 37 | 136.8995 | 7.1157 | 266.535933 |
| bewid | 2016-10-03 | 225.04373 | 1450 | 9.402827 | 0.9866242 | 16.991021 | be | 49.91430 | 5.50560 | 38 | 239.0371 | 9.3435 | 7254.638284 |
| bewid | 2016-10-04 | 239.79297 | 1332 | 19.426088 | 0.9441434 | 13.967379 | be | 49.91430 | 5.50560 | 39 | 283.9859 | 12.8557 | 3657.004677 |
| bewid | 2016-10-05 | 230.06026 | 834 | 11.788528 | 0.9790562 | 13.080366 | be | 49.91430 | 5.50560 | 40 | 254.2855 | 11.5161 | 1364.676793 |
| bewid | 2016-10-06 | 257.27916 | 105 | 8.802455 | 0.9882680 | 12.896498 | be | 49.91430 | 5.50560 | 41 | 262.6920 | 9.0394 | 108.901618 |
| bewid | 2016-10-07 | 225.45666 | 783 | 11.733415 | 0.9792495 | 8.789353 | be | 49.91430 | 5.50560 | 42 | 241.4463 | 5.8445 | 619.645176 |
| bewid | 2016-10-08 | 220.72492 | 763 | 7.550870 | 0.9913536 | 10.643082 | be | 49.91430 | 5.50560 | 43 | 216.0022 | 4.5372 | 1329.472197 |
| bezav | 2016-09-19 | 230.53787 | 179 | 20.840915 | 0.9359864 | 5.930753 | be | 50.90550 | 4.45500 | 46 | 248.0609 | 2.5992 | 101.503188 |
| bezav | 2016-09-20 | 246.49968 | 208 | 16.678516 | 0.9585168 | 9.246014 | be | 50.90550 | 4.45500 | 47 | 274.1445 | 3.0758 | 157.680622 |
| bezav | 2016-09-21 | 270.98637 | 166 | 40.950706 | 0.7745945 | 5.787027 | be | 50.90550 | 4.45500 | 48 | 356.9570 | 5.8386 | 103.501184 |
| bezav | 2016-09-22 | 97.59338 | 102 | 56.593004 | 0.6139698 | 6.679753 | be | 50.90550 | 4.45500 | 49 | 73.7458 | 4.6781 | 62.617825 |
| bezav | 2016-09-23 | 258.55974 | 124 | 26.777050 | 0.8965449 | 4.719645 | be | 50.90550 | 4.45500 | 50 | 39.6559 | 3.2851 | 50.657193 |
| bezav | 2016-09-24 | 344.43365 | 45 | 10.411509 | 0.9836254 | 9.448950 | be | 50.90550 | 4.45500 | 51 | 11.1378 | 10.9241 | 37.297609 |
| bezav | 2016-09-25 | 115.41968 | 2 | 41.816437 | 0.7661868 | 7.626110 | be | 50.90550 | 4.45500 | 52 | 102.2711 | 7.0907 | 35.754717 |
| bezav | 2016-09-26 | 228.46210 | 326 | 11.395800 | 0.9804149 | 8.941989 | be | 50.90550 | 4.45500 | 53 | 24.5415 | 2.0060 | 224.634208 |
| bezav | 2016-09-29 | 67.80032 | 31 | 8.117199 | 0.9900147 | 18.024483 | be | 50.90550 | 4.45500 | 56 | 86.4486 | 9.6427 | 53.161287 |
| bezav | 2016-09-30 | 344.56839 | 79 | 60.391419 | 0.5737918 | 2.792418 | be | 50.90550 | 4.45500 | 57 | 33.4194 | 4.8668 | 29.786944 |
| bezav | 2016-10-01 | 59.77601 | 1 | 0.000000 | 1.0000000 | 10.908599 | be | 50.90550 | 4.45500 | 58 | 88.8316 | 8.5546 | 23.424203 |
| bezav | 2016-10-02 | 171.75234 | 160 | 18.500990 | 0.9492024 | 14.745879 | be | 50.90550 | 4.45500 | 59 | 153.2538 | 6.2164 | 171.662466 |
| bezav | 2016-10-03 | 227.32931 | 696 | 7.175984 | 0.9921876 | 18.196879 | be | 50.90550 | 4.45500 | 60 | 246.6762 | 9.2640 | 1908.871242 |
| bezav | 2016-10-04 | 241.36444 | 325 | 7.457851 | 0.9915644 | 19.591541 | be | 50.90550 | 4.45500 | 61 | 280.0947 | 14.3929 | 1210.425432 |
| bezav | 2016-10-05 | 228.62355 | 202 | 9.117989 | 0.9874172 | 19.029156 | be | 50.90550 | 4.45500 | 62 | 256.0070 | 11.4778 | 343.676563 |
| bezav | 2016-10-06 | 223.60655 | 86 | 10.530944 | 0.9832507 | 12.649973 | be | 50.90550 | 4.45500 | 63 | 260.8913 | 9.0281 | 70.068940 |
| bezav | 2016-10-07 | 229.92090 | 208 | 9.070166 | 0.9875481 | 12.679566 | be | 50.90550 | 4.45500 | 64 | 240.4960 | 6.3306 | 190.298353 |
| bezav | 2016-10-08 | 223.43824 | 299 | 6.400964 | 0.9937790 | 12.772825 | be | 50.90550 | 4.45500 | 65 | 231.0196 | 4.5301 | 283.075678 |
| bgvar | 2016-09-19 | 79.91751 | 550 | 37.550679 | 0.8067323 | 7.879590 | bg | 43.27694 | 27.79750 | 68 | 63.2977 | 5.3904 | 290.301391 |
| bgvar | 2016-09-20 | 186.56640 | 491 | 14.132990 | 0.9700357 | 8.867101 | bg | 43.27694 | 27.79750 | 69 | 176.3777 | 7.4880 | 347.651638 |
| bgvar | 2016-09-21 | 146.27627 | 765 | 30.550604 | 0.8674862 | 6.752325 | bg | 43.27694 | 27.79750 | 70 | 151.6641 | 4.0340 | 317.363287 |
| bgvar | 2016-09-22 | 154.18897 | 925 | 9.829450 | 0.9853920 | 10.483469 | bg | 43.27694 | 27.79750 | 71 | 160.2234 | 7.9331 | 857.457377 |
| bgvar | 2016-09-23 | 164.55559 | 894 | 8.485949 | 0.9890920 | 10.810841 | bg | 43.27694 | 27.79750 | 72 | 156.5309 | 2.5067 | 711.142363 |
| bgvar | 2016-09-24 | 176.39335 | 686 | 58.750880 | 0.5911303 | 5.624746 | bg | 43.27694 | 27.79750 | 73 | 171.0250 | 2.3907 | 260.442607 |
| bgvar | 2016-09-25 | 204.75006 | 519 | 27.329989 | 0.8924688 | 6.010558 | bg | 43.27694 | 27.79750 | 74 | 243.6880 | 6.5292 | 184.801448 |
| bgvar | 2016-09-26 | 205.14935 | 802 | 24.519145 | 0.9125009 | 8.343486 | bg | 43.27694 | 27.79750 | 75 | 239.2367 | 4.8639 | 440.850936 |
| bgvar | 2016-09-27 | 174.21314 | 864 | 11.451435 | 0.9802251 | 8.432454 | bg | 43.27694 | 27.79750 | 76 | 167.8201 | 5.2708 | 491.207189 |
| bgvar | 2016-09-28 | 130.71927 | 762 | 16.114227 | 0.9612221 | 6.133133 | bg | 43.27694 | 27.79750 | 77 | 107.2343 | 5.0023 | 377.757353 |
| bgvar | 2016-09-29 | 158.19053 | 763 | 25.096133 | 0.9085308 | 6.897309 | bg | 43.27694 | 27.79750 | 78 | 171.7575 | 5.2570 | 314.937614 |
| bgvar | 2016-09-30 | 344.91125 | 498 | 44.434040 | 0.7402881 | 4.402306 | bg | 43.27694 | 27.79750 | 79 | 345.7172 | 4.2410 | 139.474316 |
| bgvar | 2016-10-01 | 174.56548 | 616 | 84.495779 | 0.3370872 | 5.101490 | bg | 43.27694 | 27.79750 | 80 | 70.2830 | 2.1711 | 150.015555 |
| bgvar | 2016-10-02 | 189.51616 | 795 | 148.551348 | 0.0346980 | 5.544111 | bg | 43.27694 | 27.79750 | 81 | 15.1775 | 4.5769 | 277.742215 |
| bgvar | 2016-10-03 | 135.43227 | 788 | 7.796389 | 0.9907848 | 7.552862 | bg | 43.27694 | 27.79750 | 82 | 97.1934 | 4.0028 | 454.503413 |
| bgvar | 2016-10-04 | 115.22533 | 151 | 37.427717 | 0.8078659 | 6.900251 | bg | 43.27694 | 27.79750 | 83 | 106.3692 | 6.5256 | 56.919915 |
| bgvar | 2016-10-05 | 119.65315 | 336 | 44.448636 | 0.7401418 | 6.392374 | bg | 43.27694 | 27.79750 | 84 | 18.3218 | 0.5821 | 172.406629 |
| bgvar | 2016-10-06 | 269.05749 | 410 | 54.627835 | 0.6347528 | 2.246930 | bg | 43.27694 | 27.79750 | 85 | 41.7714 | 2.3945 | 78.297053 |
| bgvar | 2016-10-07 | 330.84571 | 102 | 42.949853 | 0.7550568 | 5.136493 | bg | 43.27694 | 27.79750 | 86 | 358.7805 | 4.8110 | 41.575375 |
| bgvar | 2016-10-08 | 164.48223 | 912 | 37.180201 | 0.8101413 | 7.415488 | bg | 43.27694 | 27.79750 | 87 | 122.0190 | 6.7857 | 783.129351 |
| ctcdv | 2016-09-19 | 225.81792 | 171 | 15.741366 | 0.9629626 | 7.354205 | ct | 41.60192 | 1.40283 | 90 | 157.1206 | 3.7762 | 50.300229 |
| ctcdv | 2016-09-20 | 249.90305 | 285 | 25.306607 | 0.9070641 | 8.053629 | ct | 41.60192 | 1.40283 | 91 | 128.4062 | 4.9797 | 85.422525 |
| ctcdv | 2016-09-21 | 203.60017 | 386 | 27.465975 | 0.8914565 | 8.585782 | ct | 41.60192 | 1.40283 | 92 | 140.1784 | 3.2601 | 130.105595 |
| ctcdv | 2016-09-22 | 248.41284 | 254 | 35.136696 | 0.8285829 | 6.575098 | ct | 41.60192 | 1.40283 | 93 | 28.5372 | 1.4114 | 54.712616 |
| ctcdv | 2016-09-23 | 233.82611 | 286 | 64.181561 | 0.5339777 | 5.026455 | ct | 41.60192 | 1.40283 | 94 | 291.0176 | 1.3314 | 52.137492 |
| ctcdv | 2016-09-24 | 266.69419 | 111 | 44.120858 | 0.7434217 | 6.141204 | ct | 41.60192 | 1.40283 | 95 | 8.9263 | 3.5241 | 28.155396 |
| ctcdv | 2016-09-25 | 242.81950 | 98 | 75.448650 | 0.4202031 | 3.305072 | ct | 41.60192 | 1.40283 | 96 | 162.4338 | 2.3981 | 10.551261 |
| ctcdv | 2016-09-26 | 199.89681 | 201 | 20.141302 | 0.9400827 | 6.478780 | ct | 41.60192 | 1.40283 | 97 | 162.9579 | 3.5626 | 54.201482 |
| ctcdv | 2016-09-27 | 229.83114 | 399 | 47.001660 | 0.7142847 | 7.528194 | ct | 41.60192 | 1.40283 | 98 | 222.1203 | 6.3500 | 100.292123 |
| ctcdv | 2016-09-28 | 211.40794 | 431 | 20.720518 | 0.9367000 | 6.822220 | ct | 41.60192 | 1.40283 | 99 | 249.5237 | 1.8675 | 96.275932 |
| ctcdv | 2016-09-29 | 123.56584 | 182 | 114.888169 | 0.1339395 | 2.533676 | ct | 41.60192 | 1.40283 | 100 | 35.2140 | 3.5561 | 19.327251 |
| ctcdv | 2016-09-30 | 281.59635 | 68 | 64.273261 | 0.5330206 | 2.951832 | ct | 41.60192 | 1.40283 | 101 | 41.5120 | 3.8675 | 11.105006 |
| ctcdv | 2016-10-01 | 58.16377 | 83 | 24.248483 | 0.9143372 | 4.827528 | ct | 41.60192 | 1.40283 | 102 | 91.3354 | 3.5157 | 16.551225 |
| ctcdv | 2016-10-02 | 288.64671 | 122 | 65.180255 | 0.5235733 | 2.290329 | ct | 41.60192 | 1.40283 | 103 | 65.5916 | 0.9144 | 20.209016 |
| ctcdv | 2016-10-03 | 193.35739 | 841 | 35.934881 | 0.8214546 | 5.780609 | ct | 41.60192 | 1.40283 | 104 | 82.3490 | 2.5949 | 176.944515 |
| ctcdv | 2016-10-04 | 234.80288 | 672 | 45.588661 | 0.7286607 | 6.154008 | ct | 41.60192 | 1.40283 | 105 | 198.0870 | 2.2743 | 142.899845 |
| ctcdv | 2016-10-05 | 188.53995 | 422 | 41.905028 | 0.7653218 | 5.848155 | ct | 41.60192 | 1.40283 | 106 | 124.9322 | 5.4432 | 78.400512 |
| ctcdv | 2016-10-06 | 88.96194 | 204 | 60.093176 | 0.5769408 | 4.853551 | ct | 41.60192 | 1.40283 | 107 | 109.1070 | 4.8240 | 61.217372 |
| ctcdv | 2016-10-07 | 160.02349 | 377 | 15.304075 | 0.9649558 | 8.491793 | ct | 41.60192 | 1.40283 | 108 | 113.0683 | 7.4302 | 126.073352 |
| ctcdv | 2016-10-08 | 155.32398 | 360 | 35.828324 | 0.8224119 | 6.733815 | ct | 41.60192 | 1.40283 | 109 | 169.6555 | 3.0838 | 75.330612 |
| ctpda | 2016-09-19 | 210.37811 | 273 | 20.398254 | 0.9385924 | 6.949115 | ct | 41.88882 | 2.99717 | 112 | 165.8521 | 4.9333 | 67.787572 |
| ctpda | 2016-09-20 | 199.52288 | 441 | 8.126046 | 0.9899931 | 10.588659 | ct | 41.88882 | 2.99717 | 113 | 140.0032 | 5.3067 | 135.406686 |
| ctpda | 2016-09-21 | 183.39599 | 151 | 24.044490 | 0.9157102 | 7.078381 | ct | 41.88882 | 2.99717 | 114 | 165.6452 | 3.1302 | 40.579692 |
| ctpda | 2016-09-22 | 151.01691 | 18 | 23.126656 | 0.9217685 | 3.751680 | ct | 41.88882 | 2.99717 | 115 | 342.4081 | 1.6897 | 8.090695 |
Plot mean directions per site over date:
Plot mean directions per site:
Total mean direction per site
Aggregate directions overall mean per site. Still filtering out points with a dens of less then 5 and only night. NOTE: now averages all heights and all times.
Uses same circular mean, sd and rho as above.
##Calculate overall mean direction per site
flyway_night %>% #Start from the flyway all nights
filter(dens >5) %>% #Filter out all time stamps with dens less than 5
group_by(radar_id) %>% #For each site, calculate:
summarize(
total_avg_dd = mean.circular(
circular(dd, units = "degrees"), na.rm = TRUE), #Mean direction
total_dd_sd = deg(sd.circular(
circular(dd, units = "degrees"), na.rm = TRUE)), #SD of mean dir
rho_dd = rho.circular(
circular(dd, units="degrees"), na.rm=TRUE), #R of mean dir
total_avg_ff = mean(ff, na.rm = TRUE)) %>% #Mean groundspeed
mutate(country = substring(radar_id,1,2) #Add country
)->mean_dir_total
#-Add lat and long-
mean_dir_total <-merge(mean_dir_total, radar_metadata[, c("radar_id","latitude", "longitude")], by = "radar_id")
##Total mean wind direction per site
wind %>% #Start with all wind data
group_by(radar_id)%>% #For each site calculate:
summarize(
total_avg_wspeed = mean(wind.speed), #Mean windspeed
total_avg_wdir = mean.circular(
circular(wind_dir_to, units = "degrees")), #Mean wind dir
total_wdir_sd = deg(sd.circular(
circular(wind_dir_to, units = "degrees"))) #Mean sd
)->total_mean_wind
#-Add wind to mean_dir_total-
mean_dir_total <-merge(mean_dir_total, total_mean_wind[, c("radar_id","total_avg_wspeed", "total_avg_wdir", "total_wdir_sd")], by = "radar_id")
##Total mean MTR per site
flyway_agg_mtr %>% #From the MTR per site and night
group_by(radar_id)%>% #For each site, calculate:
summarize(
total_mean_MTR= mean(mean_MTR) #Mean MTR
)->total_mean_MTR
#-Add MTR to directions and wind
mean_dir_total <-merge(mean_dir_total, total_mean_MTR[, c("radar_id","total_mean_MTR")], by = "radar_id")
Plot total mean direction and mean MTR on map:
Plot mean directions per site and date on map
Mean dir per each night (black arrows, Note! now sized by MTR, not R). Red dots also sized by MTR, green arrows show wind direction sized by wind speed:
Weather effects
Calculate wind assitance
Tail wind component
We start with calculating an overall (system level) measure of tailwind component (TC) by using the overall mean direction at each site and the nightly mean wind direction. This will of course be a very blunt measure of wind assistance as it uses track rather than heading (does not take drift/comp into account) and further does not take into account nightly variation in preferred directions. Needs review?
Equation used:
\[TC = V_{wind} * \cos(\alpha _{wind}- \bar{\alpha}_{track})\]
Wind profit
We also calculate wind assistance in a slightly different way, “wind profit” (WP) sensu Erni et al 2002. We need to assume a mean speed (or should we use the site specific one?). Now uses 12 m/s….
Equation used:
\[WP = 12 -\sqrt{12^2 + V^2_{wind}-2V_{wind}*12*\cos(\alpha _{wind}- \bar{\alpha}_{track})}\]
#Add total mean dir per site to MTR means (using MTR to have full dataset, not filtered by dens)
MTR_wind <-merge(flyway_agg_mtr, mean_dir_total[, c("radar_id","total_avg_dd")], by = "radar_id")
MTR_wind <-merge(MTR_wind, wind, by = c("radar_id","date_of_sunset"))
MTR_wind%>%
mutate(country=(substr(radar_id,1,2)))%>%
mutate(total_avg_dd = ifelse(total_avg_dd < 0, (total_avg_dd+360),
total_avg_dd)) %>% #Change from -180 - 180, to: 0 - 360
mutate(diff = abs(wind_dir_to-total_avg_dd))%>% #Calc diff between mean dir and nightly wind dir
mutate(diff = ifelse(diff > 180, (abs(diff-360)),
diff))%>% #Ajust angels over 180
mutate(tail_comp = wind.speed*cos(diff*pi/180))%>% #Calc TC (times pi/180 bc radians)
mutate(wind_profit = 12-sqrt(12^2+wind.speed^2-2*wind.speed*12*cos(diff*pi/180)) #Calc WP (times pi/180 bc radians)
)->MTR_WP
Plot mean nightly MTR over tailwind component:
Plot of only positive tail-wind conditions over time, to compare to latitudinal wave. Size and color by tail wind component (only positive).
Plot mean nightly MTR over wind profit:
Mixed model
In a mixed model, test the how tailwind component affects the mean MTR per site. Included in the model are: random effects: radar_id and country, variables: rain or not (rainYN), tailwind component (tail_comp) and the previous nights tail wind component (lagTC) and the interaction between tail_comp and lagTC. Rain is included to account for the fact that we know that on nights with rain there must per definition be less migration, and because we are also cutting out data when its raining. Rain “yes” is here defined as if 40% or more time stamps during a night are classified as “raining” according to the definition used above (5 altitude bins with DBZH >7). The response variable is log(mean_MTR) because mean MTR is not normally distributed, and the residuals are also skewed if mean MTR is not logged (I tried several different models, with logged data or not, and the results seem consistent). I tested that the model got better (lower AIC) when adding each variable.
#Create dataset with rainYN and tailwind comp of previous night
MTR_WP%>%
mutate(rainYN = ifelse(percent_rain>40, "yes", "no"))%>%
group_by(radar_id)%>%
mutate(lagTC = lag(tail_comp))->c
#Linear mixed model (load lmerTest to get p-values)
library(lmerTest)
mtr_model1= lmer(log(mean_MTR+1) ~ tail_comp +rainYN +lagTC +tail_comp:lagTC +(1|radar_id) +(1|country), data=c)
summary(mtr_model1)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula:
## log(mean_MTR + 1) ~ tail_comp + rainYN + lagTC + tail_comp:lagTC +
## (1 | radar_id) + (1 | country)
## Data: c
##
## REML criterion at convergence: 4873.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1210 -0.4821 0.0906 0.6445 4.5007
##
## Random effects:
## Groups Name Variance Std.Dev.
## radar_id (Intercept) 0.325 0.5701
## country (Intercept) 1.085 1.0416
## Residual 2.088 1.4449
## Number of obs: 1322, groups: radar_id, 70; country, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.679e+00 3.340e-01 1.030e+01 14.009 4.77e-08 ***
## tail_comp 8.572e-02 8.629e-03 1.250e+03 9.934 < 2e-16 ***
## rainYNyes -1.948e+00 1.584e-01 1.267e+03 -12.292 < 2e-16 ***
## lagTC -1.424e-02 8.751e-03 1.250e+03 -1.627 0.104
## tail_comp:lagTC -6.686e-03 9.192e-04 1.300e+03 -7.273 6.04e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tl_cmp rnYNys lagTC
## tail_comp -0.002
## rainYNyes -0.030 0.033
## lagTC -0.001 -0.699 0.079
## tl_cmp:lgTC -0.074 -0.036 -0.097 -0.052